Nov 05, 2012

Data Visualization: That Was Simple!

[ Many thanks to Ahmad AbouAmmo for this great blog post. I've cross-posted it from his blog. ]

Data Visualization: That Was Simple!

Working with data is not about numbers; it is about how to creatively present it. Digital media professionals process lots of data on a daily basis. Analyzing this data allows us to find important trends and information, which can be shared with customers (and of course senior management). There are many ways to present the data, such as a simple table and results ? boring, or creatively visualize it with images, maps, and other graphics.

One of my favorite ways to visualize data is Google Fusion Tables. I will discuss in this blog how to create a map that shows Obama and Romney twitter mentions in the Middle East and visualize the data on a Google map.

Step 1:

Before the word visualization comes the word data. Finding a specific data, whether based on a hunch, or trends, is the first step. There are many ways to do so, and one of them is social media monitoring tools. These tools are perfect to collect massive social media data based on many variables, such as demographics, location, keywords, etc. There are many monitoring tools available in the market, such as SalesForce Marketing Cloud (formerly Radian6), Lithium, Sysomos, and others. In this blog, I will be using Sysomos MAP.

I won’t be discussing in this blog how to use social media monitoring (I will leave this to another blog). I will, however, provide my search criteria:
1. Search for the word “Obama” in English and Arabic in each country in the Middle East
2. Search for the word “Romney” in English and Arabic in each country in the Middle East
3. For this search, I will provide a 6 months period
4. I will only record twitter mentions
5. Make note of any specific keywords in each country related to each candidate

After collecting the data, I usually save it in an Excel sheet, which allows me to do further analysis if I need to. You can download the excel sheet here.

Step 2:

Review your data. What are the most important trends? Which country is the one with most tweets? What is the most important keyword for each candidate? Asking few ideas here help to know the way the data will be visualized in Google fusion tables.

Step 3:

Okay. Now we have the data, and we know what we want to visualize. The fun part begins:

1. Go to this link: drive.google.com
2. Login using your Google account (did I mention you should have an account?)
3. In the main window, go to the “Create” button on the top left
4. Click on “More” and then “Fusion Table (Experimental)” <– don’t let this scare you
5. Upload your data. In my case, I will upload my data from the excel sheet I created (You can download the excel sheet I created here to use for your test)
6. Once uploaded, click on “Map of Geometry”
7. Google fusion will locate the countries on the map and add their related data
8. Skip to step 17 if you do not want to highlight the countries’ boundaries

Step 9 to 16 is advanced:

9. To highlight the countries that contain the data, we need to locate another table that contains borders information. Here is a link for a good library
10. Once in the new table, click on “Visualize”, and then “Map”
11. Copy the URL

12. Go to the your original table and click on “File”, then “Merge”
13. Paste the link of the boundaries table data into the “Or paste a web address here:” and then click “Next”
14. Choose corresponding columns that contain similar data. In our case, it is Country and Name. Click “Next”, “Merge”, and then “View Table”

15. The new “fused” table is created. Now click on “Map” to view that data visualized on the map. You may need to zoom in
16. You can improve how the highlights look like. Go to “Tools”, and then to “Change map styles”

Publishing:

17. To control the data showing, you have to be in the Map view, click on “Table”, then “Change Info Window Layout”
18. You may add and delete any data you want to show in the info window

19. Once you are done, click on “Tools”, “Publish”. You will have to make the data public by changing its visibility